Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Learning Algorithm on Assets' Behaviour During and Post Covid Pandemic

Global financial assets behaviour has become highly volatile during the pandemic period, especially the highly risky assets. Financial instruments like cryptocurrencies are basically speculative and the investors basically trade on these anomalies. Even though the entire world has come to standstill these markets were never. In order to understand the market anomalies during the COVID pandemic the popular asset in cryptos which is bitcoin along with the global market index such as S&P 500, Global Crude Oil prices and gold prices daily trading data are taken into consideration during and post covid. Some of the interesting aspects of Machine Learning (ML) such as variety of techniques, parameter selection, nonlinearity and generalization ability make it well suited for the problems of uncertain functional structure. Price prediction of stock markets is a challenging problem because of unpredictable noise and the number of potential variables that may impact on the prices. The research work presented in this paper involves the development of a ML algorithm which will throw light on the price behaviour of these instruments during and post crisis. © 2022 WMSCI.All rights reserved.

Electric Vehicle Charging Behavior Prediction using Machine Learning Models

The global spread of the COVID-19 pandemic has significantly impacted the electric vehicle (EV) industry. The lockdown restriction has resulted in a significant shift in the use of public charging infrastructures. This paper investigates the effects of COVID-19 on electric vehicle users' charging behavior before, after, and during COVID-19 lockdown restrictions, using the data from a public charging facility from the City of California. In this study, we performed data visualization using K-means and hierarchical clustering analysis. This work uses the vehicle's connection and disconnection time to identify common charging pattern identification and charging behavior where K-means clustering outperforms the hierarchical clustering for all three different scenarios modelled. In addition, prediction of collective charging session duration is achieved using Machine Learning Models, Random Forest and XgBoost. We achieved a mean absolute percentage error (MAPE) of 0.146 and 0.151 percent for XgBoost and Random Forest respectively. © 2022 IEEE.

A Machine Learning Model for Effective Consumer Behaviour Prediction

Effective consumer behavior prediction can play a crucial role in online marketing, especially in the COVID19 scenario. In this work, we have analyzed consumer behavior to understand consumer needs and predict future requirements. For the same, we have applied the machine learning models on an amazon dataset collected from Kaggle. The dataset consists of reviewers' comments, ratings, many other parameters for the product. The model's outcome indicates that the proposed Random Forest model performs exceptionally well, and its Accuracy is approx. 98.73%. A comparative study has been done to show the efficacy of the work, and it has been observed that the performance of the proposed model is quite remarkable, and it can be a competent model for effective consumer behavior prediction. © 2021 IEEE.